Details on Dataset

This analysis will be on the earning potential of various college majors after graduation. The data is from the American Community Survey 2010-2012 Public Use Microdata Series. It contains data from a survey of college graduates and the majors they graduated in, along with the links to earning potential and their employment information. It spans 173 individual undergraduate majors across 16 major categories.

Primary Objectives

  • Find out which major categories are the most popular among students
  • Find the gender breakdown for each major category
  • Find out which individual majors are the most popular (Top 15)
  • Find the gender breakdown for both sets of major rankings
  • Analyze the median earning potential across major categories
  • Analyze the median earning potential for the Top 15 majors
  • Analyze the distribution of median earnings across all majors
  • Visualize the effect of taking different sample sizes has on the average median earnings
  • Visualize the effect different sampling methods have on the average median earnings
  • Analyze unemployment rates of Engineering and Computers & Mathematics majors

Major Category Analysis

The following will be an analysis of the overall major categories with total students, gender breakdowns of each category.

Initial Hypotheses

  1. The Social Sciences and Humanities will be the overall top categories as they are by far the most broad in scope, hence more majors would come under their umbrella. This would mean they would be over-represented in the data.

  2. The Engineering, Computer Science, and Physical Sciences major categories are expected to be less popular because of their demanding coursework requirements

  3. Women will be over-represented in the Social Sciences and Humanities, while Men will be over-represented in Engineering and Physical Science categories.

Major Category Gender Distribution

Findings

  • I was surprised that Business was quite equivalent, as I had thought more men would gravitate towards it than women

  • My guess that women would be over-represented in the Humanities was correct (~441,000 Women to ~273,000 Men)

  • The split in the Social Science category was far more even than I thought it would be (~273,000 Women to ~257,000 Men)

  • I was correct in guessing that men would be over-represented in Engineering (~408,000 Men to ~129,000 Women)

  • I was surprised by the relatively even split in the Physical Sciences (~95,000 Men to ~90,000 Women), as I had guessed that men would be over-represented in this field

Individual Major Analysis

The following will be an analysis of the most popular majors, with total students and gender breakdown.

Initial Hypotheses

  1. The majors from the Social Sciences or Humanities category will likely take up the most spots on the Top 15

  2. The majors from the Engineering category will be underrepresented on the chart with only one or two being in the Top 15

  3. Due to how unpopular the Computers and Mathematics major category was, I believe that none of those majors will be seen in the top 15

  4. I feel that women will hold a majority of the top majors, due to the relatively even gender split in the Business major category, along with the majorities they hold in the Humanities, Education, Psychology and Social Work, and Health categories

Top 15 Majors Based on Total Students

Findings

  1. Psychology is by far the most popular major with ~394,000 students

  2. Contrary to my expectations, majors from the Social Sciences and Humanities did not hold a majority of the top 15 positions

  3. There was more variety in the major category of the top 15 majors, but Business stands out with 5 positions out of 15, the most for any single major category

  • This makes sense seeing as Business is the most popular major category among students

  1. As I expected, no major from Computers and Mathematics were present in the top 15

  2. I did not expect that no Engineering majors made it into the top 15 seeing as Engineering is the fourth most popular major category.

Gender Distribution for Top 15 Majors

Findings

  1. Women are by and large the majority in:
  • Psychology (~307,000 Women to ~87,000 Men)
  • Communications (~143,000 Women to ~71,000 Men)
  • Nursing (~188,000 Women to ~22,000 Men)
  • Elementary Education (~158,000 Women to ~13,000 Men)
  • General Education (~117,000 Women to ~27,000 Men)

  1. Among the top 15 majors, only in the Finance major do men have a significant majority (~115,000 Men to ~59,000 Women)

  2. The numbers are relatively even in the remaining majors, with Men and Women trading majority by small amounts

  3. I had guessed that women would hold the majority among the most popular majors, and I was correct in my assumption

  • Psychology and Social Work (Psychology), Health (Nursing), and Education (Elementary Education and General Education) were the specific fields I noted as most likely being mainly comprised of women

Median Earnings Analysis

Initial Hypotheses

  1. Due to Business being the most popular major category, it will be in the Top 3 for earning potential

  2. Engineering will most likely hold the top spot due to specialized fields paying well

  3. Computers and Mathematics will be in the Top 3 due to high salaries in the IT/Tech sector

  4. The most popular major categories other than Business will be on the lower paying side (median or below median)

Median Earnings By Major Category

Findings

  1. As I suspected, Engineering was at the very top of the list with the highest median pay among all major categories ($57,000) with earnings ranging from $50,000 at the 25th percentile, to $60,000 at the 75th percentile.

  2. As I predicted, Computers and Mathematics students earned the second highest median pay of $45,000

  3. Business, the most popular major category, earns a median pay of $40,000 with earnings ranging from $38,000 at the 25th percentile, $47,750 at the 75th percentile, and a max of $62,000

  4. The lowest paying majors are in the Psychology and Social work category

  5. The highest paying major (Petroleum Engineering, $110,000) and the lowest paying major (Library Science, $22,000) are both outliers

Central Limit Theorem

Here we will take a look into the median earnings distribution for the whole data set, along with the applicability of the Central Limit Theorem.

Median Earnings Distribution

As can be seen in the distribution above, the median earnings seem to be normally distributed, as can be expected with earnings, it is skewed right by high earning majors like those in Engineering, Computers and Mathematics, and Business.

## Mean Median Earnings: $ 40151
## Standard Deviation: $ 11470

Differing Sample Sizes

This section will show the plots of different sample sizes and how they might affect the mean of the

## Sample Size: 10   Sample Mean:  40272.24      SD:  3701.887
## Sample Size: 20   Sample Mean:  40245.49      SD:  2669.355
## Sample Size: 30   Sample Mean:  40199.46      SD:  2194.172
## Sample Size: 40   Sample Mean:  40183.79      SD:  1874.693

Findings

As can be seen in the sample size plots above, the plots all look very similar to the overall median earning distribution with the average median earnings being $40,151 . We can see that as the sample size increases, we notice that the sample mean distribution stays roughly the same and it moves towards the population mean of the data set.

Differing Sampling Method

This section will

## Overall Population    Sample Mean: 40151      SD: 11470
## Simple Random Sampling    Sample Mean: 40658      SD: 12365
## Stratified Sampling   Sample Mean: 40588      SD: 11665
## Systematic Sampling   Sample Mean: 40280      SD: 11457

Findings

From the above charts with the different sampling methods we can see that with different sampling methods, we still see the mean sticking right around the $40,000 range.

Data Wrangling

This is the section where we use data wrangling techniques to analyze our dataset

Findings

From the chart above, we can see that Nuclear Engineering majors have the highest unemployment rate (~0.178), which makes sense due to the highly specialized nature of the field and what would most likely be in a market with limited opportunities. I was surprised to see that Mathematics and Computer Science majors had practically no unemployment, but with two very intensive quantitative STEM majors, a lot of opportunities would be available in many different fields. It looks like traditional Engineering majors face far less unemployment rates than those in Computers and Mathematics majors. I would have suspected the opposite to be true, given that software development jobs are ubiquitous, and seeing Computer Programming and Data Processing, so high up at a time when the Data Science craze was kicking off was surprising.